Klasifikasi kualitas air dengan menggunakan metode support vector machine
Abstract
Water quality is very important for life to maintain the sustainability of environmental ecosystems in waters. This research focuses on the use of the Support Vector Machine Method or SVM as a classification method for monitoring and classifying water quality. The data used is water quality index data sourced from kaggle.com, amounting to 8000 data with various attributes. Through the training and testing process using the SVM method, accuracy results reached 94.24%. The model evaluation results in the good class for the precision value were 97% with a recall of 91% and in the not good class the precision value was 92% with a recall of 98%. Thus, overall the model using the SVM method can categorize water quality well. So the results of this research can help the government monitor water quality more effectively and more quickly on water conditions.
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References
Afrah, A. S., Sari, N. F. A. T., Utama, S. N., Holle, K. F. H., Lestandy, M., Sintiya, E. S., & Rizdania. (2024). Comparative Study of Machine learning and holt-winters exponential smoothing models for prediction of CPI’s seasonal data. IEEE Xplore, 144–148. https://doi.org/10.1109/ICoSEIT60086.2024.10497509
Angellina, Herwindiati, D. E., & Hendryli, J. (2023). Performa support vector machine pada klasifikasi lahan dan air tanah. Jurnal Media Informatika Budidarma, 231–241. https://doi.org/10.30865/mib.v7i1.5279
Djana, M. (2023). Analisis kualitas air dalam pemenuhan kebutuhan air bersih di Kecamatan Natar Hajimena Lampung Selatan. Jurnal Redoks, 8(1), 81–87. https://doi.org/10.31851/redoks.v8i1.11853
Hartanti, D., & Ichsan, A. (2023). Komparasi algoritma machine learning dalam identifikasi kualitas air. SMARTICS Journal, 9(1), 1–6. https://doi.org/10.21067/smartics.v9i1.8113
Hermawan, A. (2019). SPKU: Sistem Prediksi Kualitas Udara (Studi Kasus: DKI Jakarta). (Tugas Akhir Naskah Publik, Universitas Teknologi Yogykarta). http://eprints.uty.ac.id/3552/
Jasman, T. Z., Fadhlullah, M. A., Pratama, A. L., & Rismayani, R. (2022). Analisis algoritma gradient boosting, adaboost dan catboost dalam klasifikasi kualitas air. JuTISI: Jurnal Teknik Informatika dan Sistem Informasi, 8(2), 392–402. https://doi.org/10.28932/jutisi.v8i2.4906
Jayadi, B. V., Handhayani, T., & Lauro, M. D. (2023). Perbandingan KNN dan SVM untuk klasifikasi kualitas udara di Jakarta. JIKSI: Jurnal Ilmu Komputer dan Sistem Informasi, 11(1). 1–7. https://doi.org/10.24912/jiksi.v11i2.26006
Okprana, H., & Winanjaya, R. (2022). Analisis Pengaruh komposisi data training dan testing terhadap akurasi algoritma Resilient Backpropagation (RProp). Brahmana: Jurnal Penerapan Kecerdasan Buatan, 4(1), 89–95. https://tunasbangsa.ac.id/pkm/index.php/brahmana/article/view/138
Piyadasa, T. D., & Gunawardana, K. (2023). A review on oversampling techniques for solving the data imbalance problem in classification. ICTer: International Journal on Advances in ICT for Emerging Regions, 16(1), 22–31. https://doi.org/10.4038/icter.v16i1.7260
Putri, S. S. M., Arhami, M., & Hendrawaty. (2023). Penerapan metode SVM pada klasifikasi kualitas air. JAISE: Journal of Artificial Intelligence and Software Engineering, 3(2). 94–101. http://dx.doi.org/10.30811/jaise.v3i2.4630
Sahi, M., Faisal, M., Arif, Y. M., & Crysdian, C. (2023). Analysis of the use of artificial neural network models in predicting Bitcoin prices. AISM: Applied Information System and Management, 6(2), 91–96. https://doi.org/10.15408/aism.v6i2.29648
Sari, N. F. A. T., Nabela, M., & Abdurrohman, M. F. (2023). Utilizing the K-means algorithm for breast cancer diagnosis: A promising approach for improved early detection. MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 15(2), 72–78. https://doi.org/10.18860/mat.v15i2.23644
Savitri, L., & Nursalim, R. (2023). Klasifikasi kualitas air minum menggunakan penerapan algoritma machine learning dengan pendekatan supervised learning. Diophantine Journal of Mathematics and Its Applications, 2(1), 30–36. https://doi.org/10.33369/diophantine.v2i01.28260
Sudin, A., Salmin, M., Fhadli, M., & Mamonto, A. M. (2023). Klasifikasi kelayakan air minum bagi tubuh manusia menggunakan metode support vektor machine dengan backward elimination. Jurnal Jaringan dan Teknologi Informasi, 2(1), 87–95. https://e-journal.unkhair.ac.id/index.php/jati/article/view/61
Ulum, S., Alifa, R. F., Rizkika, P., & Rozikin, C. (2023). Perbandingan performa algoritma KNN dan SVM dalam klasifikasi kelayakan air minum. Generation Journal, 7(2), 141–146. https://doi.org/10.29407/gj.v7i2.20270
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